Deep Convolutional Neural Networks in prestack seismic–-two exploratory examples

TitleDeep Convolutional Neural Networks in prestack seismic–-two exploratory examples
Publication TypeConference
Year of Publication2018
AuthorsAli Siahkoohi, Mathias Louboutin, Rajiv Kumar, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Page2196-2200
Month10
KeywordsDispersion, machine learning, Modeling, Processing, SEG, SRME
Abstract

Deep convolutional neural networks are having quite an impact and have resulted in step changes in the capabilities of image and voice recognition systems and there are indications they may have a similar impact on post-stack seismic images. While these developments are certainly important, we are after all dealing with an imaging problem involving an unknown earth and not completely understood physics. When the imaging step itself is not handled properly, this may possibly offset gains offered by deep learning. Motivated by our work on deep convolutional neural networks in seismic data reconstruction, we discuss how generative networks can be employed in prestack problems ranging from the relatively mondane removal of the effects of the free surface to dealing with the complex effects of numerical dispersion in time-domain finite differences. While the results we present are preliminary, there are strong indications that generative deep convolutional neural networks can have a major impact on complex tasks in prestack seismic data processing and modeling for inversion.

Notes

(SEG, Anaheim)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2018/siahkoohi2018SEGcnn/siahkoohi2018SEGcnn.html
DOI10.1190/segam2018-2998599.1
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2018/sia...

Citation Keysiahkoohi2018SEGcnn